This specification relates to collaborative filtering in recommender systems.
Recommender systems attempt to predict which items from a set of items a user may be interested in. One or more of the predicted items are then presented as recommendations to the user. For example, a content provider can present recommendations automatically or can present recommendations in response to a user request (e.g., over the Web). The recommender system predicts the items using information about the user, for example, from a profile of the user or from items in which the user has previously expressed interest.
Collaborative filtering is one technique used to predict items in which a user may be interested. Collaborative filtering models user preferences using community data. The community data includes, for example, interests and behaviors of a set of users. Generally, these models are built using static user and item sets. Periodically, the model is rebuilt to account for changes in the user set (e.g., addition or removal of users) and the item set (e.g., addition or removal of items).